There are currently many avenues for users to consume media content. In addition to traditional, non-interactive avenues such traditional television, radio, or projection screens in movie theatres, new electronic devices provide additional avenues to consume media content, such as streaming content over the Internet via computers, smart phones, or tablets. Many of these avenues for consuming media content are primarily supported by advertising revenues. However, for potential advertisers, determining the impact of particular advertisements can be difficult to determine. Additionally, producers of media content have interest in determining consumer's reactions to the media content they produce.
A factor that increases the difficulty of measuring the impact of specific media content are the second screen devices that are commonly used by consumers of media content. Many users will consume media content while having easy access to a smart phone, a tablet computer, a laptop or another electronic device. Thus, it is possible for a user to be in the vicinity of presented media content and not be engaged with the presented media at all.
The new avenues for consuming media content also provide additional opportunities for users to interact with the content providers and have access to personalized services. One option for producers or distributors of media content to provide personalized services is through a recommendation engine. Such engines select new media content to recommend to the user based on information known about a user. Increasing the amount of information that a recommendation engine has concerning a specific user increases the accuracy of recommendation engine to correctly recommend media content that the user will find interesting. As a result, gathering information concerning what media content a user finds engaging and what media content a user does not find interesting is important to providing a good user experience.